Next Article in Journal
The Importance of Phosphate Control in Chronic Kidney Disease
Previous Article in Journal
Pre- and Post-Migration Influences on Weight Management Behaviours before and during Pregnancy: Perceptions of African Migrant Women in England
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Design and Development of a Food Composition Database for an Electronic Tool to Assess Food Intake in New Caledonian Families

by
Juliana Chen
1,*,
Solène Bertrand
2,3,
Olivier Galy
2,
David Raubenheimer
4,
Margaret Allman-Farinelli
1 and
Corinne Caillaud
5
1
Discipline of Nutrition and Dietetics, Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Camperdown, NSW 2006, Australia
2
Interdisciplinary Laboratory of Research in Education, University of New Caledonia, 98851 Noumea, New Caledonia
3
Pacific Community, 98800 Noumea, New Caledonia
4
Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Camperdown, NSW 2006, Australia
5
Discipline of Biomedical Informatics and Digital Health, Charles Perkins Centre, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Nutrients 2021, 13(5), 1668; https://doi.org/10.3390/nu13051668
Submission received: 27 March 2021 / Revised: 26 April 2021 / Accepted: 11 May 2021 / Published: 14 May 2021
(This article belongs to the Section Nutrition Methodology & Assessment)

Abstract

:
The food environment in New Caledonia is undergoing a transition, with movement away from traditional diets towards processed and discretionary foods and beverages. This study aimed to develop an up-to-date food composition database that could be used to analyze food and nutritional intake data of New Caledonian children and adults. Development of this database occurred in three phases: Phase 1, updating and expanding the number of food items to represent current food supply; Phase 2, refining the database items and naming and assigning portion size images for food items; Phase 3, ensuring comprehensive nutrient values for all foods, including saturated fat and total sugar. The final New Caledonian database comprised a total of 972 food items, with 40 associated food categories and 25 nutrient values and 615 items with portion size images. To improve the searchability of the database, the names of 593 food items were shortened and synonyms or alternate spelling were included for 462 foods. Once integrated into a mobile app-based multiple-pass 24-h recall tool, named iRecall.24, this country-specific food composition database would support the assessment of food and nutritional intakes of families in New Caledonia, in a cross-sectional and longitudinal manner, and with translational opportunities for use across the wider Pacific region.

1. Introduction

Countries and territories across the Pacific Islands face the triple burden of malnutrition from undernutrition, micronutrient deficiencies, as well as overweight and obesity, associated with non-communicable diseases (NCDs). From the Global Burden of Disease study, the prevalence of overweight and obesity across the Oceanic Pacific Island countries and territories are among the highest globally, for adults (as high as 88.3% for women), and for children (as high as 66.1% for girls) [1].
New Caledonia is a French overseas territory in the South Pacific with a population size of 271,407 [2]. The multiethnic society of New Caledonia is representative of the Pacific population, and consists primarily of Melanesians (indigenous Kanak people, 39.1%), Europeans (27.1%), Polynesians (8.2%), and Asians (2.7%) [3]. Studies in New Caledonia have found the BMI to be significantly higher among Melanesian and Polynesian adolescents as compared with those of European descent [4]. In particular, adolescent overweight was associated with Melanesian ethnicity, living in rural areas, and being of lower socioeconomic status [5]. Similar trends between ethnicity and weight have also been found to be present in adults [6].
NCDs associated with unhealthy diets, such as cardiovascular disease, diabetes, and cancer are the leading causes of mortality, contributing to over 80% of all deaths in the Pacific [7,8] and more than 50% of mortality in New Caledonia [9]. In Pacific communities, overweight and NCD progression have been accelerated by transitioning food environments facilitated by globalization and trade [10,11,12,13,14] combined with a decline in local agricultural and fishery practices [15]. Consequently, traditional nutrient-dense plant-based diets, particularly starchy tuber staples [16], have been replaced with imported energy-dense nutrient-poor processed foods and convenience fast foods high in saturated fat, added sugar, and/or added salt [8,14].
Nevertheless, nutrition transitions are poorly captured [17], and there is little understanding about specific foods and nutrients consumed by the New Caledonian population in general, let alone by ethnicity and geographic location. Our recent studies showed that both in rural and urban areas of New Caledonia, processed food is omnipresent in the diets of Melanesian adolescents, and that high proportions of children are drinking sugar sweetened beverages (SSBs) and energy drinks [16,18,19]. Trade data have revealed that SSBs [20] and processed foods make up the majority of imports to Pacific Island countries and territories [13] and New Caledonia [21]. The New Caledonian 2008 Household Expenditure and Income Survey [22] provides data on the inventory of foods purchased, produced, consumed away from home, and traded on a household level [17,23] but detailed information on the nutritional intake of individuals is required.
With the exception of Fiji [24], for the majority of low to middle-income countries in the Pacific Island countries and territories, including New Caledonia, no national nutrition survey has ever been conducted. With advancements in technology, smartphone nutrition applications (apps) for mobile devices are a convenient and valid way to survey diets at the population level [25,26,27,28,29]. Furthermore, electronic self-administered 24-h recall tools exist and have been validated for use in both children and adult populations (e.g., ASA-24 [30,31], Intake24 [32,33], and myfood24 [34,35]). However, none have been designed for the Pacific Island countries and territories, including New Caledonia. The backend to such dietary assessment tools for measuring and assessing diets is having a food composition database that reflects the food supply of the country [36,37]. The Food and Agricultural Organization (FAO) Pacific Islands Food Composition Tables, Second Edition was developed in 2004 [38], providing a nutrient composition data for close to 900 foods of the Pacific region. A feasibility study for updating to a third edition of the Pacific database was undertaken in 2019 with six countries, but no publicly available updates have been released and New Caledonia was not an included country in the scoping study.
New Caledonia was selected for this study, with its advantage of a representative Pacific population, and sectors of the population who may still follow a largely traditional Pacific lifestyle. The French and European influence of the New Caledonian food supply also provides a backdrop for assessing the impact of “Western” foods on the traditional diet. Therefore, this study aimed to develop a fit-for-purpose food composition database to analyze the food and nutritional intake of New Caledonian children and adults.

2. Materials and Methods

2.1. Context of the Electronic Tool

The New Caledonian food composition database described in the remainder of this paper was developed for integration into the backend of a mobile app-based 24-h recall tool, i.e., iRecall.24, that could be used to collect dietary information of children and adults. The iRecall.24 app functions in a similar way as other electronic self-administered 24-h recall tools (e.g., ASA-24 [39], Intake24 [40], and myfood24 [41]), whereby users undertake the multiple-pass 24-h recall process. The multiple-pass method involves users recalling their food intake over the previous 24 h according to five steps, as depicted in Figure 1. In the iRecall.24 app, firstly, the user provides a quick list of foods and beverages consumed at each eating occasion during the previous day. Details are collected about the time and context of each eating occasion including when and where they ate, who they ate with, and the presence of screen use (e.g., from television, smartphone, etc.). Then, users provide details about the foods and beverages consumed, by selecting corresponding foods from the New Caledonian database, estimating portion sizes of foods consumed, and reviewing items entered. Multiple probes are implemented in the app at each step to ensure that commonly forgotten foods and beverages (e.g., condiments, sweet and savory discretionary foods, soft drinks, and alcohol) have been captured and that users are able to add any additional items. Users are provided with a final opportunity to review and confirm the foods and beverages entered and a final probe for any other forgotten items is conducted. For the purposes of collecting dietary data in New Caledonia, the iRecall.24 app version 1.0 was developed in French, with corresponding French language version of the New Caledonian food composition database used. The development of the iRecall.24 app is described elsewhere.

2.2. Database Development

The FAO Pacific Islands Food Composition Tables, Second Edition, 2004 (Pacific database) [38] formed the foundation for the development of this food composition database for New Caledonia. A collaboration between accredited New Caledonian and Australian dietitians and other researchers modified the original Pacific database to ensure it captured the contemporary food environment in New Caledonia, thereby enabling measurement of nutrients from food and beverage (hereafter referred to as food) intake and for future longitudinal assessment of the nutrition transitions among the New Caledonian population. This database was compiled originally in English, but food names and modifications were completed in French for uploading into the iRecall.24 app. The Tables de composition des aliments du Pacifique, developed by The Pacific Community in 2003 [42], was used for corresponding French food name translations to those in the Pacific database.
This New Caledonia food composition database was developed in three phases (as summarized in Figure 2 below): Phase 1, initial database development to expand the range of foods; Phase 2, refinement of database items and naming and assignment of portion size images for food items; Phase 3, imputation of missing nutrient values for food items, with a focus on saturated fat and total sugar.

2.2.1. Phase 1: Initial Database Development

Initial cleaning of the Pacific database was carried out to remove any duplicate food items. The original Pacific database was expanded to better reflect the current food supply in New Caledonia. The original Pacific database had not been updated in more than 15 years, and therefore it was necessary to update food items to reflect the contemporary food environment associated with the nutrition transition.

Sourcing of Food Items

With no previous national nutrition survey to provide guidance on foods consumed by the New Caledonian population, additional food items for inclusion were gathered from supermarket inventories, visits to local food outlets, and local practicing dietitians with knowledge of traditional recipes and specialty items commonly consumed.
A list of branded items for sale in New Caledonia was obtained from the websites of major supermarkets. From this, 6163 were identified as food items. Nutritional supplements and weight loss products were excluded. The food items were coded into 66 categories. Food item categories determined from the supermarket data were compared to those of the individual food items and categories in the Pacific database. Where it was identified that there was inadequate representation of the food category or specific items, these were added into the database.
Fieldwork in New Caledonia by the Australian and local French-speaking dietitians (J.C. and S.B.), included visits to two weekend markets, six food stalls/trucks, two popular takeaway food stores, a local school cafeteria, and three local supermarkets. According to the food categories and food items from the original Pacific database, the dietitians checked and confirmed the availability of these foods during the field visits, or collected additional details on the variety, supply, and range of other local food options if they were identified as absent in the original Pacific database. Another local dietitian (E.S.) working in New Caledonia as part of the research team contributed to a list of commonly consumed foods based on their experience with local clients and it was ensured that these food items were reflected in the database. Traditional dishes and local specialty foods that were not present in the original Pacific database were also reviewed by J.C. and S.B. and included from the Food Portions book for French Polynesia developed by the Ministry of Health, Department of Health; the House for Diabetes, Centre for Therapeutic Education; and the French Polynesian Dietitian’s Association in Papeete [43].

Sourcing Food Composition Data for Additional Food Items Identified

Nutrition information for these additional new food items were gathered from the English edition of the ANSES-CIQUAL French Food Composition Tables, version 2017 (CIQUAL) [44]; the Australian Food, Supplement, and Nutrient Database (AUSNUT) 2011–2013 [45]; the 2018 New Zealand FOODFiles database [46]; and the U.S. Department of Agriculture (USDA) FoodData Central 2019 [47]. The country of origin of the food item and matching based on food descriptors were considered to determine which food composition data source was selected. Food labelling information was also used to derive nutrition information for popular items in New Caledonia where the food was not available in the databases (e.g., Maggi seasoning sauce). For mixed dishes in the French Polynesia portions book, nutrition information were calculated based on ingredient lists from recipe formulations provided with this resource [43]. Nutrient information for individual ingredients were derived primarily from the original Pacific database, as well as the CIQUAL [44] or AUSNUT [45] databases.

2.2.2. Phase 2: Refinement Based on Feedback

Usability Testing

The French version of the database from Phase 1 was imported into the backend of the purpose-designed iRecall.24 app, for a self-administered 24-h recall. Trialing the app allowed users to experience the range of foods and names of foods and portion sizes in the food composition database. Adopting a ”think aloud” approach for usability testing [48], users from our research team, including three dietitians local to New Caledonia or from the French Island territories, Wallis, and Futuna; two researchers; and two developers from the app company tested the app in French and provided feedback on the database-related functionality of the app.
On the basis of the feedback, food items that were raw or would not be consumed in their uncooked state were removed and replaced by cooked varieties. It was also evident from usability testing that users did not necessarily know about specific varieties of foods (especially for fruits, vegetables, and seafood). To minimize confusion about which food item to select when accessing the database within the iRecall.24 app, different varieties or cultivars of the same food (e.g., for jackfruit, A. heterophyllus, and A. integer) were combined into a single item, either by replacement with an existing composite or generic item from the original Pacific database or by averaging nutrient compositions. The FAO/INFOODS Guidelines for Food Matching Version 1.2 [49] was used to guide this process. Food items that were unavailable or unlikely to be consumed in New Caledonia were removed. This method has been used in the development of other nutrition databases [36] and is supported by qualitative findings that support the usability of food databases within nutrition apps [36,50,51].

Modifying Food Nomenclature

The naming of food items within the Pacific database, and other database sources (e.g., CIQUAL) were quite detailed to capture the taxonomic or scientific naming of certain species of foods (e.g., green leaves and fish and seafood) as well as cooking methods. Consequently, the French names of foods were shortened and simplified from the long string generic names to improve searchability within the iRecall.24 app. This involved, simplifying specific descriptors of the content of the food items, such as the specific ingredients within the food, particularly if there was only one common option available in New Caledonia. Where one food item description included multiple cooking methods, such as “boiled, microwaved, steamed, or poached”, the most common method used in New Caledonia was chosen. The modification of the food nomenclature was guided by the food and meal preparation knowledge the French-speaking dietitian local to New Caledonia (S.B.).
Where food items had been derived from English databases (e.g., AUSNUT, FOODFiles, and USDA FoodData Central), Google translate was used for translations into French, and then simplified. Synonyms and alternate spellings were added to food items where appropriate to improve the ability to locate food items consumed.
All modifications to the database and French naming of foods were reviewed and checked by two independent French speaking members of our research team (S.B. and C.C.), one being a French dietitian with public health and community experience in improving nutrition with Pacific Island communities.

Portion Size Images

All food items (food and beverages) in the New Caledonian database were assigned portion size images and/or household or gram measures. Portion size images were available in two formats for users to select from (Figure 3): (1) as served on a plate with three to seven images options corresponding to different amounts of food (e.g., noodles) or (2) in a range of sizes (e.g., different lengths of baguette or different volumes of a beverage in a glass) or in a group with different varieties (e.g., assorted biscuits or soft drink/energy drink cans).

2.2.3. Phase 3: Ensuring Comprehensive Nutrition Information

Saturated Fat and Total Sugar Nutrient Information

The original Pacific database consisted of food composition values for energy including fiber (in kilojoules (kJ) and kilocalories (kCal)), water (g), protein (g), total fat (g), carbohydrate (g), total dietary fiber (g), cholesterol (mg), as well as 15 vitamins and minerals (sodium (mg), magnesium (mg), potassium (mg), calcium (mg), iron (mg), zinc (mg), retinol (µg), beta-carotene equivalents (µg), total vitamin A equivalents (µg), thiamin (mg), riboflavin (mg), niacin (mg), vitamin B12 (µg), vitamin C (mg), and Vitamin E (mg)). However, saturated fat and total sugar were not included. Given the nutrition transition occurring in the Pacific Island region, it was deemed important to include these nutrients in this New Caledonian database. The FAO/INFOODS Guidelines for Food Matching [49] was again used to determine appropriate food matches from which saturated fat and total sugar values could be derived. This involved food identification based on food name and descriptor and assessing water and total fat and carbohydrate content.
Where available, the nutrient data for the source codes in Appendix VI, food index of the Pacific Islands food composition tables [38] were used to derive saturated fat and total sugar values of foods from the Pacific database. Saturated fat (g) and total sugar values (g) were calculated proportionally to the total fat and carbohydrate values of the matched food item, unless there was an exact match between food items (i.e., the food and all its descriptors in the source codes match exactly with the food and all its descriptors from the Pacific database) and the total fat and carbohydrate values were identical. This method was chosen over replacing all nutrient values to retain as much of the original dataset which included Pacific Islands-generated analytical data.
If the original publication of the source codes could not be located or there was no inclusion of saturated fat and total sugar values, data were borrowed and imputed in a proportional manner based on the total fat and carbohydrates from matching or similar foods or from data in scientific articles. Values from food packaging labels or calculations from ingredients in recipes were also used. Assumptions were also made, for example, where total fat or carbohydrate values were zero or where they would be not naturally occurring in a food, saturated fat and total sugar were assumed to be zero (e.g., no sugar in fresh meat, or no saturated fat in green leafy vegetables). The selection of the database for food matching was based on assessment of the quality of the food matching, the comprehensiveness of the nutrient data available, and the country most likely to supply that food to New Caledonia. Borrowed or imputed values came from the CIQUAL, AUSNUT, FOODFiles, USDA, and the Japanese food composition tables [52].

Data Cleaning and Checks

All energy values were recalculated based on the INFOODS formulas (which included energy from fiber) for kJ and kcal. Total vitamin A equivalents (retinol equivalents) were re-calculated using the INFOODS formula for all food items, as AUSNUT [53] uses a different formula to calculate provitamin A activity. For fruit and vegetable food items derived from the CIQUAL database, beta-carotene values were checked against a similar matched food from the original Pacific database or AUSNUT to confirm that values were not underestimating other beta-carotene equivalents. This was because the CIQUAL database only reported retinol and beta-carotene values, but not beta-carotene equivalents.
To improve the usability and ease of aggregated nutrient calculations for researchers or dietitians, where the Pacific or CIQUAL database had trace values (T, i.e., less than the limit of detection), these were replaced with zero values or nutrient values from matched food items from the AUSNUT or FOODFiles databases. Further data cleaning was conducted to remove the “<” sign in the CIQUAL database, and values were rounded down by 0.001, 0.01, or 0.1 decimal places, depending on the number of significant figures for that nutrient.
Data quality checks were carried out by two independent researchers, with any suspected errors reviewed against the original source food composition database.

3. Results

3.1. Phase 1

Table 1 presents the number of food items derived from different sources and their classification into the food categories across iterations during the development phases of the New Caledonian food composition database. The original Pacific database consisted of nutrition composition data for 889 foods in 20 food categories. Nine items identified as duplicates were removed. Then, 644 items were added, of which 74% (n = 476) were from the CIQUAL database; 14% (n = 93) from AUSNUT; 9% (n = 58) from the French Polynesia portions book contributing to traditional dishes and local food specialties; 1.2% (n = 8) from food packages/labels; and the remaining 1.4% (n = 9) from FOODFiles, USDA, and new generic foods or recipes plus a field for water (Table 1). Subsequently, at the end of Phase 1, the New Caledonian food composition database was expanded to a total of 1524 food items.
The original 20 food categories were expanded into 41 categories (Table 1), particularly with cereal and cereal products expanded into four categories, meat and poultry into two categories, milk and milk products into four categories, confectionery into four categories, herbs and spices into two categories, and beverages into six categories. The original processed food category was replaced with four new categories “chips and savory snacks”, “pizzas, pies and burgers”, “mixed canned foods”, and “soup”. New categories of “sandwich” and “savory canapes” were added. These changes were made to improve distinction of processed and discretionary foods from those of the core food groups.

3.2. Phase 2

In Phase 2, the main problems encountered during usability testing were around foods not found by users as they were missing from the database, or appeared in the database as a different name to what the users referred to them as (e.g., “pain au chocolat” instead of “chocolatine”), or users had entered the name of the food category instead of specific type of food (e.g., “viande” (meat) instead of beef, pork, chicken). Table 2 presents the other issues relating to database searching, including too many search returns or confusion over different varieties of foods. Spelling issues also affected the success of searches including, misspelling or variations in spelling, the presence or absence of French accents and special characters (e.g., “oeuf” instead of “œuf”), or use of plurals or singular spelling (e.g., “chou-fleur” vs. “choux-fleur”).
On the basis of the feedback, three main areas were identified for improvements. The database was expanded by an additional 185 foods to include the missing food items and additional common local foods or traditional dishes. Secondly, 737 food items and one food category (infant food and formula) were removed. This consisted of removal of the following: 422 items (57.3%) to reduce the number of food options; 80 items (10.9%) that were averaged or replaced with a composite or generic food item; 76 items (10.3%) that could not be eaten raw (e.g., starchy vegetables, some green leaves, dried legumes and lentils, raw meat and wild animal/game) or where the made up beverage option was already included (e.g., coffee or cocoa powder); 69 items (9.4%) that were not commonly available in New Caledonia; 38 items (5.1%) were baby foods (e.g., breastmilk, formula, and baby foods); 37 items (5%) were replaced with another matching food item where nutrient information was comprehensive; and 15 items (2%) which were already represented or a duplicate of another food item. The final version 1.0 of the New Caledonian food composition database contained a total of 972 food items, with 40 food categories (see Table 1, phase 2).
Finally, the names of 593 food items (61%) were simplified and shortened or edited (e.g., using brand names rather than long descriptors, for instance,“biscuit au chocolat, fourré à la crème (Oreo)” was changed to just the brand name “Oreo”), and synonyms or alternate spelling were included for 462 items (47.5%). Fuzzy string matches were employed within the revisions of the iRecall.24 app to enhance searchability by allowing for minor variations in spelling to account for misspelling, singular and plurals, accents, and spaces.
A total of 353 unique food or assorted food group portion size images were included in this New Caledonian food composition database. The same portion size images could be used for similar foods (e.g., images of brie cheese used for other soft cheeses) and ”group” layout pictures could contain between two and 23 assorted food options (e.g., different types or brands of confectionery). Therefore, the portion size images were coded to 615 (63.3%) of the 972 food items. Portion size images were sourced via open source from the online 24-h recall tool, Intake24 [54] (48.7%, n = 172); the French Polynesia portions book (45%, n = 159); and additional images for 22 unique foods were taken, particularly for green leafy vegetables, cheeses, processed meats, and baguette.

3.3. Phase 3

In phase 3, complete saturated fat and total sugar values were included for all 972 food items in version 1.0 of this New Caledonian database. For 456 (47%) food items, the saturated fat and/or total sugar values were derived from the original source of data for those items; 56 (5.7%) food items were calculated from recipes or scientific articles for saturated fat and 52 for total sugar; two food items were derived from food labels (0.2%); and 31 food items with assumed saturated fat values and 42 for total sugar (Table 3). The remaining 516 foods required matching with food items from other food composition database sources to determine the most appropriate saturated fat and total sugar values to use. Exact matches for saturated fat were present in 159 food items, while 122 foods had exact matches for total sugar, with the majority of matches coming from AUSNUT.

4. Discussion

We developed a comprehensive country-specific food composition database for New Caledonia, to be integrated into a mobile app-based multiple-pass 24-h recall tool iRecall.24. Version 1.0 of this New Caledonian database comprises a total of 972 food items covering 40 food categories and 25 nutrient values including saturated fat and total sugar. To assist with estimation of portion sizes within the iRecall.24 app, 615 food items have associated portion size images. Usability testing allowed for improvements in the searchability of the database, through refinement of the food items included, but also of food naming and spelling.
Country-specific databases have been emphasized as an important feature of the usability and accuracy of app-based dietary assessment tools, for researchers and their study population, as well as health care professionals, such as dietitians and their clients in the general public [36,37,50,55]. To reflect the changing food supply in New Caledonia, food composition data were drawn from the databases of multiple countries based on the country of origin of the imported food item. Branded and generic food items, as well as local or traditional recipes and food items, were also included within the database, with the aim of increasing the relevance of search results in the iRecall.24 app to the New Caledonian population and improve accuracy in measurement of food and nutritional intake. These methods are in alignment with the development of other databases for electronic dietary assessment tools, such as the EaT app to assess dietary intake in Australian young adults [36], as well as other online self-administered 24-h recall tools, myfoods24 [37] and Intake24 [40].
Another key focus in developing this database was to increase representation of imported processed foods. This new food composition database for New Caledonian provides access to an expanded number of processed and discretionary food items and food categories, and importantly saturated fat and total sugar values have been included as part of the nutrient data. These database considerations are particularly relevant given the prevalence of processed foods across Pacific Island countries and territories and in New Caledonia [13,14] and the diet-related NCDs associated with nutrition transitions [11]. While a previous systematic review found a high contribution of fat (particularly saturated fat), sugar, and salt in the diets of many Pacific Island countries and territories [56], the study also noted the general lack of dietary data across the Pacific and New Caledonia, and concluded that higher quality research using reliable and valid dietary assessment tools was required. High unhealthy food consumption with 27% of daily food intake was recently observed in Melanesian adolescents from New Caledonia. These dietary changes might explain the high percentage of overweight and obesity (38.1% for rural and 31.7% for urban adolescents) observed in this study, therefore, further comprehensive investigation of dietary intakes is needed [16]. In the future, the food and nutrient data within this database will be validated, in the context of the iRecall.24 app against 24-h recalls in children and adults in New Caledonia.
As with other electronic self-administered 24-h recall tools [39,40,41,57], portion size images have been coded to food items in this database, for display in the iRecall.24 app. The benefit of portion size images is that they can assist users in estimating the amount of food consumed, and therefore increase the accuracy of self-reported dietary intake data [57]. Portion size images attached to this database were derived from Intake24 [40], which sourced its images from the Young Person’s Food Atlas that has been validated in children 4–16 years old [58,59]. The images from the French Polynesia portions book and the new images taken require validation in New Caledonian children and adults in future studies.
Further usability testing is underway for the database via integration with the iRecall.24 app. The app and database will be piloted with adults and children in the field in local New Caledonian communities using similar methods adopted in field testing of other digital 24-h recall tools [60]. Users will be asked to provide feedback on the usability of the system, including completion of the system usability scale [61]. Assessments of the functionality of the database and app will be compared between participants self-administering the app versus dietitians administering the app with participants.
Application of this food composition database in the iRecall.24 app would allow greater ease of conducting assessments of individuals’ usual intake as well as regional or national nutrition surveys in New Caledonia. The contribution of processed and discretionary foods to traditional diets and nutrient intake over time can be monitored in the nutrition transition. Furthermore, in the context of public health, governments can utilize this data to inform targeted health policies and nutrition interventions to meet World Health Organization global targets for the prevention and control of NCDs [8].

Limitations and Strengths

The main limitation in the development of this New Caledonian database is that nutrient values for added items were derived from other food composition databases, imputed from other foods, or calculated based on recipes. While some of these data come from the original chemically analyzed source food composition database, direct laboratory analysis of the foods imported into New Caledonia and local or traditional foods have not been conducted. Direct chemical analysis of these food items, including saturated fat and other fatty acids, and total sugar and added versus free sugars, should be considered a priority in future updates to the database for a better understanding of micronutrient deficiencies and under- and over-nutrition and their impact on population health in New Caledonia. Drawing upon an Italian example [62], analyzing other bioactive components such as nutrient antioxidants in traditional foods and recipes may also be beneficial for greater advocacy of consuming traditional diets for NCD prevention and cultural preservation.
As no national nutrition survey has been conducted in New Caledonia and understanding about the range of foods consumed by the New Caledonian population is limited, it is possible that there are some common foods missing from this version of the database. With ever-changing product reformations and new food developments, keeping food composition databases up-to-date is a recognized challenge, even among countries, such as the UK, with well-established food composition databases [63]. Nevertheless, a strength of version 1.0 of this New Caledonian food composition database is that it has been structured in a dynamic manner that will allow for future updates and further refinements to the database once field testing has been conducted. As food consumption data are gathered, this will support additional modifications to ensure that popular foods and recipes are adequately represented in the database, and adjustments to the database can be made to include specific varieties or cultivars of food items based on popularity of intake in New Caledonia. While the food represented in this database is primarily adapted for use in New Caledonia and other French territories, there is flexibility within the database to also update the source and nutrient values based on changes in patterns of trade, product reformulations, or as updates to the original Pacific database occur. A similar process can be undertaken to personalize, adapt, and extend the database for use in other Pacific Island countries and territories, and therefore increase the translational potential of this database.

5. Conclusions

In this paper, we described the development of a food composition database that can be integrated into a mobile app-based 24-h recall tool iRecall.24. The database offers flexibility for future refinement of the food and nutrient data according to changes in the food supply. With its existing overlap with the broader Pacific Islands Food Composition Tables, this database has translational capacity for adaptation to capture dietary data across the wider Pacific region. Future research to validate the iRecall.24 app and associated food composition database and portion size images in the New Caledonian population will ensure that a robust dietary assessment tool is available for use by dietitians, researchers, and government agencies. The tool has potential to enable accurate assessment of individual and population intakes and with nutrition transitions underway, improve monitoring of changes in dietary patterns and food and macro- and micronutrient intake, including over- and under-consumption. Such data and evidence can provide the necessary evidence to develop targeted public health policies and individual and community-level nutrition intervention for children and adults in New Caledonia, to improve diets and reduce diet related health risks.

Author Contributions

Conceptualization, J.C., S.B., O.G., D.R., M.A.-F., and C.C.; methodology, J.C., M.A.-F., S.B., and C.C.; formal analysis, J.C.; data curation, J.C., S.B., C.C., O.G., and M.A.-F.; writing—original draft preparation, J.C.; writing—review and editing, J.C., S.B., O.G., D.R., M.A.-F., and C.C.; project administration, J.C. and C.C.; funding acquisition, C.C. and D.R. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded through the University of Sydney internal research grants awarded to C.C. (CIA), Pacific Fund grants “Developing Digital Tools to assess diet and physical activity in children in the Pacific Region” awarded to C.C. (CIA), O.G., D.R. and M.A.-F., and the project team from the “eHealth and Health Care Delivery Research Theme” and the “Sydney Food and Nutrition Network”.

Institutional Review Board Statement

This research described the development of the database and app. The app company personnel and researchers tested the app as is usual practice with no details collected or reported, and thus not subject to Human Ethics Review Committee approval.

Informed Consent Statement

Not applicable as company personnel and members of the research team tested the app.

Acknowledgments

The authors would like to thank the support of other dietitian members of the research team, Emilie Simonet and Anne-Lise Corbeau, for their valuable insight into local food supply and eating practices in New Caledonia. We would also like to acknowledge the support provided by European Commission through the RISE program (Research and Innovation Staff Exchange) H2020–MSCA–RISE–2019 Grant Agreement: 873185: FALAH and by the Charles Perkins research Network “Children and Adolescents Health and wellbeing in the Pacific”.

Conflicts of Interest

J.C., S.B., O.G., D.R., and C.C. declares no personal or financial conflicts of interest. M.A.-F. has developed food and nutrition-based apps for research purposes. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Ng, M.; Fleming, T.; Robinson, M.; Thomson, B.; Graetz, N.; Margono, C.; Mullany, E.C.; Biryukov, S.; Abbafati, C.; Abera, S.F. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014, 384, 766–781. [Google Scholar] [CrossRef] [Green Version]
  2. Institut de la Statistique et des Études Économiques Nouvelle-Calédonie. Populations Légales des Communes de Nouvelle-Calédonie en 2019: Recensement de la Population. Available online: https://www.insee.fr/fr/statistiques/4464931?sommaire=2122859#consulter (accessed on 7 September 2020).
  3. Institut de la Statistique et des Études Économiques Nouvelle-Calédonie. Communautés: Une Population Pluriethnique. Available online: https://www.isee.nc/population/recensement/communautes (accessed on 15 June 2020).
  4. Frayon, S.; Cherrier, S.; Cavaloc, Y.; Wattelez, G.; Lerrant, Y.; Galy, O. Relationship of body fat and body mass index in young Pacific Islanders: A cross-sectional study in European, Melanesian and Polynesian groups. Pediatr Obes 2018, 13, 357–364. [Google Scholar] [CrossRef]
  5. Frayon, S.; Cherrier, S.; Cavaloc, Y.; Touitou, A.; Zongo, P.; Wattelez, G.; Yacef, K.; Caillaud, C.; Lerrant, Y.; Galy, O. Nutrition behaviors and sociodemographic factors associated with overweight in the multi-ethnic adolescents of New Caledonia. Ethn. Health 2017, 1–17. [Google Scholar] [CrossRef] [PubMed]
  6. Corsenac, P.; Annesi-Maesano, I.; Hoy, D.; Roth, A.; Rouchon, B.; Capart, I.; Taylor, R. Overweight and obesity in New Caledonian adults: Results from measured and adjusted self-reported anthropometric data. Diabetes Res. Clin. Pract. 2017, 133, 193–203. [Google Scholar] [CrossRef] [PubMed]
  7. World Health Organisation. Obesity and Overweight. Available online: https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 1 May 2020).
  8. World Health Organisation. Western Pacific Regional Action Plan for the Prevention and Control of Noncommunicable Diseases (2014–2020). Available online: https://apps.who.int/iris/handle/10665/208175 (accessed on 10 May 2020).
  9. World Health Organisation. WHO Country Cooperation Strategy 2018–2022: New Caledonia. Available online: https://apps.who.int/iris/handle/10665/259920 (accessed on 28 February 2020).
  10. Hawkes, C. Uneven dietary development: Linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Glob. Health 2006, 2, 4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Popkin, B.M.; Horton, S.; Kim, S. The nutritional transition and diet-related chronic diseases in Asia and the Pacific. Food Nutr. Bull. 2001, 22, Supplement. [Google Scholar]
  12. Ravuvu, A.; Friel, S.; Thow, A.M.; Snowdon, W.; Wate, J. Monitoring the impact of trade agreements on national food environments: Trade imports and population nutrition risks in Fiji. Glob. Health 2017, 13, 33. [Google Scholar] [CrossRef] [PubMed]
  13. Sievert, K.; Lawrence, M.; Naika, A.; Baker, P. Processed Foods and Nutrition Transition in the Pacific: Regional Trends, Patterns and Food System Drivers. Nutrients 2019, 11, 1328. [Google Scholar] [CrossRef] [Green Version]
  14. Snowdon, W.; Raj, A.; Reeve, E.; Guerrero, R.; Fesaitu, J.; Cateine, K.; Guignet, C. Processed foods available in the Pacific Islands. Glob. Health 2013, 9, 53. [Google Scholar] [CrossRef] [Green Version]
  15. Charlton, K.E.; Russell, J.; Gorman, E.; Hanich, Q.; Delisle, A.; Campbell, B.; Bell, J. Fish, food security and health in Pacific Island countries and territories: A systematic literature review. BMC Public Health 2016, 16, 285. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Galy, O.; Paufique, E.; Nedjar-Guerre, A.; Wacalie, F.; Wattelez, G.; Le Roux, P.Y.; Ponidja, S.; Zongo, P.; Serra-Mallol, C.; Allman-Farinelli, M.; et al. Living in Rural and Urban Areas of New Caledonia: Impact on Food Consumption, Sleep Duration and Anthropometric Parameters Among Melanesian Adolescents. Nutrients 2020, 12, 2047. [Google Scholar] [CrossRef] [PubMed]
  17. Walls, H.L.; Johnston, D.; Mazalale, J.; Chirwa, E.W. Why we are still failing to measure the nutrition transition. BMJ Glob. Health 2018, 3, e000657. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Frayon, S.; Wattelez, G.; Cherrier, S.; Cavaloc, Y.; Lerrant, Y.; Galy, O. Energy drink consumption in a pluri-ethnic population of adolescents in the Pacific. PLoS ONE 2019, 14, e0214420. [Google Scholar] [CrossRef]
  19. Wattelez, G.; Frayon, S.; Cavaloc, Y.; Cherrier, S.; Lerrant, Y.; Galy, O. Sugar-Sweetened Beverage Consumption and Associated Factors in School-Going Adolescents of New Caledonia. Nutrients 2019, 11, 452. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Pak, N.; McDonald, A.M.; McKenzie, J.; Tukuitonga, C. Soft drink consumption in Pacific Island countries and territories: A review of trade data. Pac. Health Dialog 2014, 20, 59–66. [Google Scholar]
  21. Institut de la Statistique et des Études Économiques Nouvelle-Calédonie. Commerce Extérieur: Importation. Available online: https://www.isee.nc/economie-entreprises/economie-finances/commerce-exterieur (accessed on 15 June 2020).
  22. Nouvelle-Calédonie, I.d.l.s.e.d.é.é. Synthèse: Household Consumption in 2008 New Caledonia. Available online: https://spccfpstore1.blob.core.windows.net/digitallibrary-docs/files/1f/1f3aed0ca49af432f438fa439bb439c6613ff6610.pdf?sv=2015-6612-6611&sr=b&sig=k6616zbhwJbgRFMIfTbve6613hAV6613sGXGUXP6618AiZ6610ZyveQnXw%6613D&se=2020-6612-6620T6603%6613A6616%6613A6644Z&sp=r&rscc=public%6612C%6620max-age%6613D864000%864002C%864020max-stale%864003D886400&rsct=application%864002Fpdf&rscd=inline%864003B%864020filename%864003D%864022NC_HIES_862008_N864001_English.pdf%864022 (accessed on 12 June 2020).
  23. Gibson, R.S. Food consumption at the national and household levels. In Principles of Nutritional Assessment, 2nd ed.; Oxford University Press: New York, NY, USA, 2005; pp. 27–40. [Google Scholar]
  24. Schultz, J.T.; Vatucawaqa, P.; Tuivaga, J. 2004 Fiji National Nutrition Survey: Main Report. Available online: http://fijibeveragegroup.com.fj/wp-content/uploads/2014/2004/2004-NATIONAL-NUTRITION-SURVEY-FIJI.pdf (accessed on 12 June 2020).
  25. Wellard-Cole, L.; Chen, J.; Davies, A.; Wong, A.; Huynh, S.; Rangan, A.; Allman-Farinelli, M. Relative Validity of the Eat and Track (EaT) Smartphone App for Collection of Dietary Intake Data in 18-to-30-Year Olds. Nutrients 2019, 11, 621. [Google Scholar] [CrossRef] [Green Version]
  26. Rangan, A.M.; Tieleman, L.; Louie, J.C.; Tang, L.M.; Hebden, L.; Roy, R.; Kay, J.; Allman-Farinelli, M. Electronic dietary intake assessment (e-DIA): Relative validity of a mobile phone application to measure intake of food groups. Br. J. Nutr. 2016, 115, 2219–2226. [Google Scholar] [CrossRef] [Green Version]
  27. Rangan, A.M.; O’Connor, S.; Giannelli, V.; Yap, M.L.; Tang, L.M.; Roy, R.; Louie, J.C.; Hebden, L.; Kay, J.; Allman-Farinelli, M. Electronic dietary intake assessment (e-DIA): Comparison of a mobile phone digital entry app for dietary data collection with 24-hour dietary recalls. JMIR Mhealth Uhealth 2015, 3, e98. [Google Scholar] [CrossRef]
  28. Ambrosini, G.L.; Hurworth, M.; Giglia, R.; Trapp, G.; Strauss, P. Feasibility of a commercial smartphone application for dietary assessment in epidemiological research and comparison with 24-h dietary recalls. Nutr. J. 2018, 17, 5. [Google Scholar] [CrossRef]
  29. Carter, M.C.; Burley, V.J.; Nykjaer, C.; Cade, J.E. ‘My Meal Mate’ (MMM): Validation of the diet measures captured on a smartphone application to facilitate weight loss. Br. J. Nutr. 2013, 109, 539–546. [Google Scholar] [CrossRef] [Green Version]
  30. Kirkpatrick, S.I.; Subar, A.F.; Douglass, D.; Zimmerman, T.P.; Thompson, F.E.; Kahle, L.L.; George, S.M.; Dodd, K.W.; Potischman, N. Performance of the Automated Self-Administered 24-hour recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. Am. J. Clin. Nutr. 2014, 100, 233–240. [Google Scholar] [CrossRef]
  31. Thompson, F.E.; Dixit-Joshi, S.; Potischman, N.; Dodd, K.W.; Kirkpatrick, S.I.; Kushi, L.H.; Alexander, G.L.; Coleman, L.A.; Zimmerman, T.P.; Sundaram, M.E.; et al. Comparison of interviewer-administered and Automated Self-Administered 24-hour dietary recalls in 3 diverse integrated health systems. Am. J. Epidemiol. 2015, 181, 970–978. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Bradley, J.; Simpson, E.; Poliakov, I.; Matthews, J.N.; Olivier, P.; Adamson, A.J.; Foster, E. Comparison of INTAKE24 (an Online 24-h Dietary Recall Tool) with Interviewer-Led 24-h Recall in 11-24 Year-Old. Nutrients 2016, 8, 358. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Foster, E.; Lee, C.; Imamura, F.; Hollidge, S.E.; Westgate, K.L.; Venables, M.C.; Poliakov, I.; Rowland, M.K.; Osadchiy, T.; Bradley, J.C.; et al. Validity and reliability of an online self-report 24-h dietary recall method (Intake24): A doubly labelled water study and repeated-measures analysis. J. Nutr. Sci. 2019, 8, e29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Albar, S.A.; Alwan, N.A.; Evans, C.E.L.; Greenwood, D.C.; Cade, J.E. Agreement between an online dietary assessment tool (myfood24) and an interviewer-administered 24-h dietary recall in British adolescents aged 11–18 years. Br. J. Nutr. 2016, 115, 1678–1686. [Google Scholar] [CrossRef] [Green Version]
  35. Wark, P.A.; Hardie, L.J.; Frost, G.S.; Alwan, N.A.; Carter, M.; Elliott, P.; Ford, H.E.; Hancock, N.; Morris, M.A.; Mulla, U.Z.; et al. Validity of an online 24-h recall tool (myfood24) for dietary assessment in population studies: Comparison with biomarkers and standard interviews. BMC Med. 2018, 16, 136. [Google Scholar] [CrossRef] [PubMed]
  36. Wellard-Cole, L.; Potter, M.; Jung, J.J.; Chen, J.; Kay, J.; Allman-Farinelli, M. A Tool to Measure Young Adults’ Food Intake: Design and Development of an Australian Database of Foods for the Eat and Track Smartphone App. JMIR Mhealth Uhealth 2018, 6, e12136. [Google Scholar] [CrossRef]
  37. Carter, M.C.; Hancock, N.; Albar, S.A.; Brown, H.; Greenwood, D.C.; Hardie, L.J.; Frost, G.S.; Wark, P.A.; Cade, J.E. Development of a New Branded UK Food Composition Database for an Online Dietary Assessment Tool. Nutrients 2016, 8, 480. [Google Scholar] [CrossRef] [Green Version]
  38. Dignan, C.; Burlingame, B.; Kumar, S.; Aalbersberg, W. The Pacific Islands Food Composition Tables, 2nd ed.; Food and Agriculture Organization of the United Nations: Rome, Italy, 2004. [Google Scholar]
  39. Subar, A.F.; Kirkpatrick, S.I.; Mittl, B.; Zimmerman, T.P.; Thompson, F.E.; Bingley, C.; Willis, G.; Islam, N.G.; Baranowski, T.; McNutt, S.; et al. The Automated Self-Administered 24-hour dietary recall (ASA24): A resource for researchers, clinicians, and educators from the National Cancer Institute. J. Acad. Nutr. Diet. 2012, 112, 1134–1137. [Google Scholar] [CrossRef] [Green Version]
  40. Simpson, E.; Bradley, J.; Poliakov, I.; Jackson, D.; Olivier, P.; Adamson, A.J.; Foster, E. Iterative Development of an Online Dietary Recall Tool: INTAKE24. Nutrients 2017, 9, 118. [Google Scholar] [CrossRef] [Green Version]
  41. Carter, M.C.; Albar, S.A.; Morris, M.A.; Mulla, U.Z.; Hancock, N.; Evans, C.E.; Alwan, N.A.; Greenwood, D.C.; Hardie, L.J.; Frost, G.S.; et al. Development of a UK Online 24-h Dietary Assessment Tool: myfood24. Nutrients 2015, 7, 4016–4032. [Google Scholar] [CrossRef] [Green Version]
  42. Dignan, C.A.; Burlingame, B.A.; Arthur, J.M.; Quigley, R.J.; Milligan, G.C. Tables de Composition des Aliments du Pacifique; Secrétariat général de la Communauté du Pacifique (SPC): Noumea, New Caledonia, 2003. [Google Scholar]
  43. Ministère de la Santé—Direction de la Santé; La Maison Du Diabétique—Centre D’éducation Thérapeutique; Association des Diététiciens de Polynésie Française. Portions Alimentaires: Polynésie Française; ADPF: French Polynesia, France, 2015; p. 68. [Google Scholar]
  44. Agence Nationale de Sécurité Sanitaire de L’alimentation de L’environnement et du Travail. ANSES-CIQUAL French Food Composition Table Version 2017. Available online: https://ciqual.anses.fr/#/cms/download/node/20 (accessed on 8 November 2018).
  45. Food Standards Australia New Zealand. AUSNUT 2011–13 Food Nutrient Database. Available online: http://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 8 November 2018).
  46. New Zealand Institute for Plant and Food Research Limited; Ministry of Health (New Zealand). New Zealand FOODfiles™ 2018 Version 01. Available online: https://www.foodcomposition.co.nz/foodfiles/ (accessed on 5 September 2019).
  47. U.S. Department of Agriculture—Agricultural Research Service. FoodData Central. Available online: Fdc.nal.usda.gov (accessed on 5 September 2019).
  48. Nielsen, J. Usability Engineering; Morgan Kaufmann: Fremont, CA, USA, 1994. [Google Scholar]
  49. FAO. FAO/INFOODS Guidelines for Food Matching; Version 1.2; FAO: Rome, Italy, 2012. [Google Scholar]
  50. Chen, J.; Berkman, W.; Bardouh, M.; Ng, C.Y.K.; Allman-Farinelli, M. The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition 2019, 57, 208–216. [Google Scholar] [CrossRef] [PubMed]
  51. Chen, J.; Allman-Farinelli, M. Impact of Training and Integration of Apps Into Dietetic Practice on Dietitians’ Self-Efficacy With Using Mobile Health Apps and Patient Satisfaction. JMIR Mhealth Uhealth 2019, 7, e12349. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Ministry of Education, Culture, Sports, Science and Technology. Standard Tables of Food Composition in Japan—2015—(Seventh Revised Version). Available online: https://www.mext.go.jp/en/policy/science_technology/policy/title01/detail01/1374030.htm (accessed on 28 January 2020).
  53. Australian Bureau of Statistics. Australian Health Survey: Nutrition—Supplements, 2011–12. 2015. Available online: https://www.abs.gov.au/statistics/health/health-conditions-and-risks/australian-health-survey-nutrition-supplements/latest-release (accessed on 12 February 2020).
  54. Food Standards Scotland; Newcastle University. Intake24. Available online: https://intake24.co.uk/ (accessed on 27 January 2019).
  55. Chen, J.; Lieffers, J.; Bauman, A.; Hanning, R.; Allman-Farinelli, M. Designing health apps to support dietetic professional practice and their patients: Qualitative results from an international survey. JMIR Mhealth Uhealth 2017, 5, e40. [Google Scholar] [CrossRef] [PubMed]
  56. Santos, J.A.; McKenzie, B.; Trieu, K.; Farnbach, S.; Johnson, C.; Schultz, J.; Thow, A.M.; Snowdon, W.; Bell, C.; Webster, J. Contribution of fat, sugar and salt to diets in the Pacific Islands: A systematic review. Public Health Nutr. 2019, 22, 1858–1871. [Google Scholar] [CrossRef]
  57. Kirkpatrick, S.I.; Potischman, N.; Dodd, K.W.; Douglass, D.; Zimmerman, T.P.; Kahle, L.L.; Thompson, F.E.; George, S.M.; Subar, A.F. The Use of Digital Images in 24-Hour Recalls May Lead to Less Misestimation of Portion Size Compared with Traditional Interviewer-Administered Recalls. J. Nutr. 2016, 146, 2567–2573. [Google Scholar] [CrossRef]
  58. Foster, E.; Hawkins, A.; Barton, K.L.; Stamp, E.; Matthews, J.N.; Adamson, A.J. Development of food photographs for use with children aged 18 months to 16 years: Comparison against weighed food diaries—The Young Person’s Food Atlas (UK). PLoS ONE 2017, 12, e0169084. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Foster, E.; Matthews, J.N.; Lloyd, J.; Marshall, L.; Mathers, J.C.; Nelson, M.; Barton, K.L.; Wrieden, W.L.; Cornelissen, P.; Harris, J.; et al. Children’s estimates of food portion size: The development and evaluation of three portion size assessment tools for use with children. Br. J. Nutr. 2008, 99, 175–184. [Google Scholar] [CrossRef] [Green Version]
  60. Rowland, M.K.; Adamson, A.J.; Poliakov, I.; Bradley, J.; Simpson, E.; Olivier, P.; Foster, E. Field Testing of the Use of Intake24-An Online 24-Hour Dietary Recall System. Nutrients 2018, 10, 1690. [Google Scholar] [CrossRef] [Green Version]
  61. Bangor, A.; Kortum, P.; Miller, J. Determining what individual SUS scores mean: Adding an adjective rating scale. J. Usability Stud. 2009, 4, 114–123. [Google Scholar]
  62. Durazzo, A.; Lisciani, S.; Camilli, E.; Gabrielli, P.; Marconi, S.; Gambelli, L.; Aguzzi, A.; Lucarini, M.; Maiani, G.; Casale, G.; et al. Nutritional composition and antioxidant properties of traditional Italian dishes. Food Chem. 2017, 218, 70–77. [Google Scholar] [CrossRef]
  63. Traka, M.H.; Plumb, J.; Berry, R.; Pinchen, H.; Finglas, P.M. Maintaining and updating food composition datasets for multiple users and novel technologies: Current challenges from a UK perspective. Nutr. Bull. 2020, 45, 230–240. [Google Scholar] [CrossRef]
Figure 1. The multiple-pass 24-h recall process for the iRecall.24 app.
Figure 1. The multiple-pass 24-h recall process for the iRecall.24 app.
Nutrients 13 01668 g001
Figure 2. Phases in the development of the New Caledonian food composition database to support the iRecall.24 app.
Figure 2. Phases in the development of the New Caledonian food composition database to support the iRecall.24 app.
Nutrients 13 01668 g002
Figure 3. Formats of portion size images for foods and beverages, as served or in a group.
Figure 3. Formats of portion size images for foods and beverages, as served or in a group.
Nutrients 13 01668 g003
Table 1. Sources and categories of food items during the development phases of Version 1.0 of the New Caledonian food composition database.
Table 1. Sources and categories of food items during the development phases of Version 1.0 of the New Caledonian food composition database.
Source
Food CategoryPIFCTCIQUALAUSNUTFP Portions BookNew Zealand FOOD FilesUSDAFood Package LabelNew Generic Food or RecipeTotal Items by Category
Starchy staples
Original PIFCT71 71
Phase 169401000074
Phase 222161220236
Cereal and bread
Original PIFCT81 1 81
Phase 14713140000074
Phase 22911100010152
Crackers and crispbread
Original PIFCTN/A 2 N/A
Phase 121420000018
Phase 20570000012
Cookies and biscuits
Original PIFCTN/A 2 N/A
Phase 192520000036
Phase 281840000030
Pastries and cakes
Original PIFCTN/A 2 N/A
Phase 13125316000075
Phase 22121516000063
Green leaves
Original PIFCT66 66
Phase 166441000075
Phase 242180000152
Other vegetables
Original PIFCT73 73
Phase 1701620000088
Phase 2427210000070
Fruit
Original PIFCT87 87
Phase 187740010099
Phase 249430010057
Nuts and seeds
Original PIFCT34 34
Phase 134050000039
Phase 214070000021
Legume and legume products
Original PIFCT33 33
Phase 1351900000054
Phase 2161010100028
Fish
Original PIFCT44 44
Phase 1441835400074
Phase 23013123510064
Shellfish and seafood
Original PIFCT39 39
Phase 139252000048
Phase 227432010037
Meat and poultry
Original PIFCT84 1 84
Phase 148212010054
Phase 2211240110039
Processed and delicatessen meats & meat products
Original PIFCTN/A 2 N/A
Phase 1293102100063
Phase 2121701100031
Milk and milk products
Original PIFCT37 1 37
Phase 112360000021
Phase 24360000013
Cheese
Original PIFCTN/A 2 N/A
Phase 1144930000066
Phase 273120000040
Dairy desserts and cream
Original PIFCTN/A 2 N/A
Phase 110220000014
Phase 24430100012
Infant food and formula
Original PIFCTN/A 2 N/A
Phase 1112600000037
Phase 2000000000
Eggs
Original PIFCT10 10
Phase 110500000015
Phase 2540000009
Fats and oils
Original PIFCT14 14
Phase 1131030000026
Phase 23540100013
Processed foods 3
Original PIFCT54 54
Chips and savory snacks
Original PIFCTN/A 2 N/A
Phase 1121110000024
Phase 25540010015
Pizzas, pies and burgers
Original PIFCTN/A 2 N/A
Phase 1142300000037
Phase 261900000025
Mixed canned foods
Original PIFCTN/A 2 N/A
Phase 1520000007
Phase 2120000003
Soup
Original PIFCTN/A 2 N/A
Phase 142910000034
Phase 21860000015
Sandwich
Original PIFCTN/A 2 N/A
Phase 112504000030
Phase 211544000024
Mixed cooked dishes
Original PIFCT20 20
Phase 11717618000159
Phase 211101314000149
Savory canapes
Original PIFCTN/A 2 N/A
Phase 10615000012
Phase 20415000010
Confectionery
Original PIFCT26 1 26
Phase 1111721000031
Phase 261031000020
Chocolate
Original PIFCTN/A2 N/A
Phase 161900000025
Phase 261010000017
Spreads
Original PIFCTN/A 2 N/A
Phase 19700000016
Phase 2412000007
Nut and cereal bars
Original PIFCTN/A 2 N/A
Phase 1260000008
Phase 2030000003
Herbs and spices
Original PIFCT47 1 47
Phase 128000000028
Phase 220040000024
Condiments, sauces and dressings
Original PIFCTN/A 2 N/A
Phase 119420002027
Phase 29350002019
Beverages 3
Original PIFCT37 37
Beverage—alcoholic
Original PIFCTN/A 2 N/A
Phase 115200000017
Phase 28120000011
Beverage—coffee, tea, cocoa
Original PIFCTN/A 2 N/A
Phase 1112110000033
Phase 22630000011
Beverage—fruit concentrate, fruit drink, cordial
Original PIFCTN/A 2 N/A
Phase 13470006020
Phase 2050000005
Beverage—Juice
Original PIFCTN/A 2 N/A
Phase 1400000004
Phase 2303000006
Beverage—soft drink
Original PIFCTN/A 2 N/A
Phase 15561000017
Phase 24240000010
Beverage—water
Original PIFCTN/A 2 N/A
Phase 13150000110
Phase 2221000016
Coconut products
Original PIFCT14 14
Phase 110020000012
Phase 2402000006
Wild animals/game
Original PIFCT21 21
Phase 121200000023
Phase 2610000007
Phase 1 total items by source 880476935852821524
Phase 2 total items by source4552781644712826972
Note: PIFCT, Pacific Islands Food Composition Tables, Second Edition [38]; CIQUAL, ANSES-CIQUAL 2017 database [44]; AUSNUT, Australian Food, Supplement and Nutrient Database (AUSNUT 2011–2013) [45]; FP portions book, food portions book for French Polynesia [43]; New Zealand FOODFiles, New Zealand FOODFiles 2018 database [46]; USDA, U.S. Department of Agriculture FoodData Central 2019 [47]; N/A, not applicable; 1 the total number of items present in the original category of the Pacific Islands Food Composition Tables, Second Edition; 2 new category created in this New Caledonia food composition database; 3 original category in the Pacific Islands Food Composition Tables, Second Edition, but replaced by new sub-categories in this New Caledonia food composition database.
Table 2. Feedback provided on the database during usability testing and the actions for improvement.
Table 2. Feedback provided on the database during usability testing and the actions for improvement.
IssueFood Items
(Corresponding Food in the Database)
Improvement Actions
Not found—missing item
  • Salade verte
  • Frites de pomme de terre, maison
  • Cotelette de porc
  • Jus d’orange pressé
  • Chocolat au lait
  • Vin rosé
  • Quiche aux épinards
  • Food items added
Not found—different name or the name of a food category
  • Baguette blanche (Pain dit à la française/à l’italienne)
  • Soupe chinoise (Soupe asiatique)
  • Viande (Boeuf; porc; poulet; etc.)
  • Chocolatine (Pain au chocolat)
  • Food name refined
  • Synonyms added to the database
Not found—too many search results
  • Mozzarella (Fromage type mozzarella)
  • Food name refined
  • Food category tags included as a synonym rather than in the main name
Not found—alternate or incorrect
  • Olives noire (Olive)
  • Olives vertes (Olive)
  • Yaourt (Yogourt)
  • Brocolis (Broccoli)
  • Choux-fleur (Chou-fleur)
  • Oeuf (Œuf)
  • Boeuf (Bœuf)
  • Redbull (Red bull)
  • Pome de terre (Pomme de terre)
  • Iniame (Igname)
  • Pin (Pain)
  • Gato (Gâteau)
  • Musli (Muesli)
  • Ri, ris (Riz)
  • Epinar (Épinard)
  • Conconbre (Concombre)
  • Carote (Carotte)
  • Aricot (Haricot)
  • Ton (Thon)
  • Socisse (Saucisse)
  • Conté (Comté)
  • Piza (Pizza)
  • Alternate spelling (including variations in accents) added as synonyms
  • Fuzzy logic applied for searches in app coding to allow for slight variations in spelling, accents, punctuation, spaces and plural and singular forms of words
Confusing items
  • Not knowing what “Lait condensé” was
  • Querying relevancy of baby/infant food “Lait maternel, colostrum, 1er âge, 2eme âge”
  • Querying relevancy of items that would not be consumed raw (e.g., saucisse, crue—sausages raw)
  • Not knowing the difference between pineapples from different countries (e.g., Ananas (d’Australie) vs. Ananas (de Papouasie-Nouvelle-Guinée))
  • Not knowing how foods were cooked or what ingredient was used (e.g., Bœuf, viande hachée: ordinaire, mijotée, égouttée)
  • Irrelevant items removed
  • Average and merge similar items together to reduce options
  • Food name refined and descriptions simplified
Table 3. Data sources for saturated fat and total sugar values for food items (n = 972) in the New Caledonian food composition database.
Table 3. Data sources for saturated fat and total sugar values for food items (n = 972) in the New Caledonian food composition database.
SourceSaturated Fat%Total Sugar%
CIQUAL
  Original source value27728.527528.3
  Exact food match value40.450.5
  Proportional value525.3474.8
  Imputed proportional value20.220.2
  Averaged value40.440.4
AUSNUT
  Original source value16116.616116.6
  Exact food match value14014.411011.3
  Proportional value14615.017818.3
  Imputed proportional value192.0202.1
  Averaged value60.660.6
New Zealand FOODFiles
  Original source value121.2121.2
  Exact food match value101.050.5
  Proportional value232.4282.9
  Imputed proportional value20.220.2
USDA FoodData Central
  Original source value60.680.8
  Exact food match value40.420.2
  Proportional value121.290.9
Japanese food composition tables
  Exact food match value10.100.0
  Proportional value20.200.0
Calculated from FP portions book recipe484.9484.9
Calculated from other recipes60.660.6
Calculated from scientific article20.200.0
Food packaging label20.220.2
Assumed value313.2424.3
Total food items972100972100
FCDB, food composition database; CIQUAL, ANSES-CIQUAL 2017 database [44]; AUSNUT, Australian Food, Supplement and Nutrient Database (AUSNUT 2011–2013) [45]; New Zealand FOODFiles, New Zealand FOODFiles 2018 database [46]; USDA, U.S. Department of Agriculture FoodData Central 2019 [47]; Japanese 2015 Food composition tables [52]; FP portions book, food portions book for French Polynesia [43].
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Chen, J.; Bertrand, S.; Galy, O.; Raubenheimer, D.; Allman-Farinelli, M.; Caillaud, C. The Design and Development of a Food Composition Database for an Electronic Tool to Assess Food Intake in New Caledonian Families. Nutrients 2021, 13, 1668. https://doi.org/10.3390/nu13051668

AMA Style

Chen J, Bertrand S, Galy O, Raubenheimer D, Allman-Farinelli M, Caillaud C. The Design and Development of a Food Composition Database for an Electronic Tool to Assess Food Intake in New Caledonian Families. Nutrients. 2021; 13(5):1668. https://doi.org/10.3390/nu13051668

Chicago/Turabian Style

Chen, Juliana, Solène Bertrand, Olivier Galy, David Raubenheimer, Margaret Allman-Farinelli, and Corinne Caillaud. 2021. "The Design and Development of a Food Composition Database for an Electronic Tool to Assess Food Intake in New Caledonian Families" Nutrients 13, no. 5: 1668. https://doi.org/10.3390/nu13051668

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop